Acquired above variable clouds, aerial images contain the components of ground reflection and cloud effect. Due to the non‐uniformity, clouds in aerial images are even harder to remove than haze in terrestrial images. This paper proposes a divide‐and‐conquer scheme to remove the thin translucent clouds in a single RGB aerial image. Based on colour attenuation prior, we design a kind of veiling metric that indicates the local concentration of clouds effectively. By this metric, an aerial image containing thickness‐varied clouds is segmented into multiple regions. Each region is veiled by clouds of nearly‐equal concentration, and hence subject to common assumptions, such as boundary constraint on transmission. The atmospheric light in each region is estimated by the modified local colour‐line model and composed into a spatially‐varying airlight map for the entire image. Then scene transmission is estimated and further refined by a weighted ‐norm based contextual regularization. Finally, we recover ground reflection via the atmospheric scattering model. We verify our cloud removal method on a number of aerial images containing thin clouds and compare our results with classical single‐image dehazing methods and the state‐of‐the‐art learning‐based declouding method, respectively.

中文翻译：

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去除单个RGB航拍图像的薄云

在可变云以上获取的航空影像包含地面反射和云效应的成分。由于不均匀性，航空影像中的云甚至比地面影像中的霾更难去除。本文提出了一种分治法，以去除单个RGB航拍图像中的半透明薄云。在先验色彩衰减的基础上，我们设计了一种衡量指标，可以有效指示云的局部浓度。通过此度量，将包含厚度变化的云的航空影像分割成多个区域。每个区域都被几乎相等集中的云遮盖着，因此要遵循共同的假设，例如传输的边界约束。每个区域的大气光都是通过修改后的局部色线模型估算的，并组成整个图像的空间变化的航空图。然后，通过加权对场景传输进行估计并进一步完善基于规范的上下文正则化。最后，我们通过大气散射模型恢复地面反射。我们在许多包含薄云的航拍图像上验证了我们的除云方法，并将我们的结果分别与经典的单图像除雾方法和基于最新学习的除云方法进行了比较。